The average consumer tends to establish certain routines and practices, i.e., habits, associated with his or her daily activities. As one example, the average consumer typically has identifiable shopping habits, such as making the majority of their purchases from a set of retailers that are typically are located within a definable “local” geographical shopping area for the consumer. In addition, the average consumer, often does their shopping, and frequents their local geographical shopping area, or a portion of their local geographical shopping area, with identifiable regularity, i.e., the average consumer often does their shopping in a given category, such as groceries, on the same day(s) of the week, and often at about the same time. As an example, a given consumer may buy coffee from a given coffee vendor located in a specific mini-mall each weekday around 7 AM, frequent a specific sandwich shop in a commercial building 3 to 4 times a week around noon, buy groceries from the same supermarket each Sunday between 4 PM and 7 PM, and frequent a given movie theater in a specific mall about every other Saturday evening around 9 PM.
The ability to determine a given consumer's shopping habits, including the geographic shopping area frequented by the consumer for making purchases and the days of the week/times the consumer typically shops in an identified geographic shopping area is of considerable value to multiple parties including, but not limited to: retailers and/or business owners and/or sellers of services and products, who could use the consumer shopping habit information to try and target/attract consumer's known to frequent the vicinity of their stores and to determine optimal hours of operation; marketers, who could also use the consumer shopping habit information to try and target/attract consumer's in a given geographical shopping area and/or at a defined time; developers, who could use the consumer shopping habit information to determine commercial and/or residential property placement and use; retail chain owners, who could use the consumer shopping habit information to determine store placement, hours of operation, and use; and other parties associated with business and business development.
In light of the considerable value of consumer shopping habit information, it is not surprising that some methods for “predicting” a consumer's shopping habits are currently available. However, these currently available methods are typically highly generalized, static, and truly are “estimates” based on rather unsophisticated assumptions, rather than actual empirical data.
For instance, some currently available methods for defining a given consumer's geographic shopping area involve obtaining a given consumer's address, and/or zip code, and then mechanically “predicting” the consumer's geographic shopping area by projecting a predefined distance/radius from the consumer's address, and/or zip code, and sweeping out a circle, i.e., by declaring the area enclosed by a circle of a predefined radius, and centered on the consumer's residence and/or zip code location, to be the consumer's geographic shopping area. While this method has the appeal of simplicity, it often fails to accurately predict and/or reflect reality because this method fails to take into account several variables and/or realities such as: fastest routes to a shopping area that may dictate that, in terms of time, the closest shopping is a outside the predefined radius/area; convenience of multiple stores being at a single location such as a mall that may be outside the predefined radius/area, but that still save the consumer time by allowing a single trip and a single stop; physical limitations such as lakes, oceans, parks, developments, hills, and mountains that can make large portions of a statically predicted geographical shopping area unrealistic and, some cases, nonsensical; consumer preferences such as desirable and/or non-desirable neighborhoods and/or ethnic based neighborhoods for special shopping needs such as a city's Chinatown, little Italy, or other area having specialty shops and languages; and/or numerous other factors that often make currently available methods for defining a given consumer's geographic shopping area unreliable and/or unrealistic.
As another example, some currently available methods for defining a given consumer's shopping habits use highly generalized estimates to predict when a given consumer might conduct their shopping. These predictions often make very general assumptions such as shopping at malls will be done on the weekend, coffee will be purchased in the morning before 9 AM, and this all within the statically predicted geographic shopping area. As might be expected, these types of predictions often prove incorrect.
As a result of the situation discussed above, consumer shopping habit information is currently more guesswork than science. Therefore, this potentially very valuable source of data is currently not being utilized to it's full potential.